Camera Calibration¶
In [1]:
import numpy as np
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
In [2]:
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/*.jpg')
plt.figure(figsize=(15,10))
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = mpimg.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (9,6), corners, ret)
#write_name = 'corners_found'+str(idx)+'.jpg'
#cv2.imwrite(write_name, img)
plt.subplot(5,4,idx)
plt.imshow(img)
plt.axis('off')
Calculate and Test undistortion¶
In [3]:
import pickle
%matplotlib inline
# Test undistortion on an image
img = mpimg.imread('camera_cal/test.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
plt.savefig('output_images/undistort_output.png')
Undistort Raw Images¶
In [4]:
def undistort(img):
"""This Fucntion removes distortion in the Input Image.
Args:
img: 3D-Image
returns:
Undistorted Version of Input Image
"""
return cv2.undistort(img, mtx, dist, None, mtx)
# Visualize undistortion
img = mpimg.imread('test_images/straight_lines1.jpg')
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(undistort(img))
ax2.set_title('Undistorted Image', fontsize=30)
plt.savefig('./output_images/test1.jpg')
Perspective Transform¶
In [5]:
def unwarp(img):
"""This Fucntion calculates the perspective transform of Input Image.
Args:
img: Undistorted Input Image
returns:
warped: Perspective transform of the input image
Minv: Inverse Perspective Matrix
"""
img_size=(img.shape[1],img.shape[0]) # Size of the Undistorted Image
imshape=img.shape
#Source Points
src = np.float32([[600,452],[691,452],[275,680],[1050,680]])
#Destination points
dst = np.float32([[330,0],[1050,0],[270,img.shape[0]],[1060,img.shape[0]]])
#Obtain Perspective tansform matrix
M=cv2.getPerspectiveTransform(src,dst)
#Apply Perspective transform
warped=cv2.warpPerspective(img,M,img_size,flags=cv2.INTER_LINEAR)
#Obtain Inverse Perspective transform matrix
Minv=cv2.getPerspectiveTransform(dst,src)
return warped,Minv
#Visualization of Unwarped Images
#images = glob.glob('test_images/*.jpg')
#unwarped_images=[]
#for idx, fname in enumerate(images):
#img = mpimg.imread(fname)
img = mpimg.imread('test_images/straight_lines1.jpg')
undistorted_image=undistort(img)
unwarped_image,nom =unwarp(undistorted_image)
src = np.float32([[600,452],[691,452],[275,680],[1050,680]])
dst = np.float32([[330,0],[1050,0],[270,img.shape[0]],[1060,img.shape[0]]])
cv2.line(img,(600,452),(691,452),[0,0,255], 7)
cv2.line(img, (691,452), (1050,680),[0,0,255], 7)
cv2.line(img,(1050,680),(275,680),[0,0,255], 7)
cv2.line(img, (275,680),(600,452) , [0,0,255], 7)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
unwarped_image=cv2.rectangle(unwarped_image,(270,0),(1030,720),(0,0,255),7)
ax2.imshow(unwarped_image)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Color & Gradient Thresholding¶
Color Spaces¶
In [6]:
def hls_image(img):
"""
This Fucntion Converts input Image to HLS color space
Args:
img: Undistorted Input Image
returns:
Image in HLS color space
"""
return cv2.cvtColor(img,cv2.COLOR_RGB2HLS)
def hsv_image(img):
"""
This Fucntion Converts input Image to HSV color space
Args:
img: Undistorted Input Image
returns:
Image in HSV color space
"""
return cv2.cvtColor(img,cv2.COLOR_RGB2HSV)
#Visualization of Input Image in different Color Spaces
#img = mpimg.imread('test_images/test1.jpg')
img = mpimg.imread('clip/frame1035.jpg')
undistorted_image=undistort(img)
gray=cv2.cvtColor(undistorted_image,cv2.COLOR_RGB2GRAY)
#RGB Space
r=undistorted_image[:,:,0]
g=undistorted_image[:,:,1]
b=undistorted_image[:,:,2]
#HLS Space
hls=hls_image(undistorted_image)
h_hls=hls[:,:,0]
l_hls=hls[:,:,1]
s_hls=hls[:,:,2]
#HSV Space
hsv=hsv_image(undistorted_image)
h_hsv=hsv[:,:,0]
s_hsv=hsv[:,:,1]
v_hsv=hsv[:,:,2]
f,axs=plt.subplots(4,3,figsize=(64,64))
ax=axs.ravel()
ax[0].imshow(gray)
ax[0].set_title("Gray image",fontsize=40)
ax[1].imshow(r)
ax[1].set_title("R Image",fontsize=40)
ax[2].imshow(g)
ax[2].set_title("G Image",fontsize=40)
ax[3].imshow(b)
ax[3].set_title("B Image",fontsize=40)
ax[4].imshow(h_hls)
ax[4].set_title("H in HLS",fontsize=40)
ax[5].imshow(l_hls)
ax[5].set_title("L Image",fontsize=40)
ax[6].imshow(s_hls)
ax[6].set_title("S in HLS",fontsize=40)
ax[7].imshow(h_hsv)
ax[7].set_title("H in HSV",fontsize=40)
ax[8].imshow(s_hsv)
ax[8].set_title("S in HSv",fontsize=40)
ax[9].imshow(v_hsv)
ax[9].set_title("V in HSV",fontsize=40)
plt.savefig('output_images/color_spaces.png')
From Above Images we can see that Lane lines are cleary visible in S-channel in HLS Transform
Color Thresholding¶
In [7]:
def binarize(img,threshold=(0,255)):
"""This Fucntion Creates a binary image of ones where threshold is met, zeros otherwise
Args:
img: Undistorted Input Image
threshold: A tuple of min and max value
returns:
binary: Binary version of the input image
"""
binary=np.zeros_like(img)
binary[(img>threshold[0]) & (img<=threshold[1])]=1
return binary
binary_gray=binarize(gray,(150,255))
binary_R=binarize(r,(150,255))
binary_G=binarize(g,(170,255))
binary_B=binarize(b,(150,230))
binary_h_hls=binarize(h_hls,(20,40))
binary_l_hls=binarize(l_hls,(50,150))
binary_s_hls=binarize(s_hls,(170,255))
binary_h_hsv=binarize(h_hsv,(20,40))
binary_s_hsv=binarize(s_hsv,(50,100))
binary_v_hsv=binarize(v_hsv,(190,255))
binary_s_gray = np.zeros_like(binary_gray)
binary_s_gray[(binary_s_hls == 1) & (binary_v_hsv==1) ] = 1
#Visualization of Color Thresholding
f,axs=plt.subplots(4,3,figsize=(64,64))
ax=axs.ravel()
ax[0].imshow(binary_gray)
ax[0].set_title("Binary Gray",fontsize=40)
ax[1].imshow(binary_R)
ax[1].set_title("Binary R",fontsize=40)
ax[2].imshow(binary_G)
ax[2].set_title("Binary G Image",fontsize=40)
ax[3].imshow(binary_B)
ax[3].set_title("Binary B Image",fontsize=40)
ax[4].imshow(binary_h_hls)
ax[4].set_title("Binary H in HLS",fontsize=40)
ax[5].imshow(binary_l_hls)
ax[5].set_title("Binary L Image",fontsize=40)
ax[6].imshow(binary_s_hls)
ax[6].set_title("Binary S in HLS",fontsize=40)
ax[7].imshow(binary_h_hsv)
ax[7].set_title("Binary H in HSV",fontsize=40)
ax[8].imshow(binary_s_hsv)
ax[8].set_title("Binary S in HSv",fontsize=40)
ax[9].imshow(binary_v_hsv)
ax[9].set_title("Binary V in HSV",fontsize=40)
ax[10].imshow(binary_s_gray)
ax[10].set_title("Combined Binary s or v",fontsize=40)
plt.savefig('output_images/color_thresholding.png')
Gradient Thresholidng¶
In [8]:
def abs_Sobel(img,orient='x',threshold=(0,255)):
"""This function Applies Sobel derivative in 'x' or 'y' orientation
Args:
img -- 3-D image or 2-D image
orient -- 'x' or 'y'
threshold -- A tuple of min and max value
returns:
binary -- Binary image of ones where threshold is met, zeros otherwise
"""
#If the image is Color image convert it to Grayscale
if len(img.shape)==3:
gray=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
else:
gray=img
# Apply x or y gradient with the OpenCV Sobel() function
if orient=='x':
sobel=cv2.Sobel(gray,cv2.CV_64F,1,0)
elif orient=='y':
sobel=cv2.Sobel(gray,cv2.CV_64F,0,1)
# take the absolute value
absolute=np.absolute(sobel)
# Rescale back to 8 bit integer
scaled_sobel=np.uint8(absolute*255/np.max(absolute))
# Create a binary image of ones where threshold is met, zeros otherwise
binary_sobel=binarize(scaled_sobel,threshold)
return binary_sobel
def mag_sobel(img,threshold=(0,255)):
"""This function calculates the magnitude of Sobel derivative
Args:
img -- 3-D image or 2-D image
threshold -- A tuple of min and max value
returns:
binary -- Binary image of ones where threshold is met, zeros otherwise
"""
#If the image is Color image convert it to Grayscale
if len(img.shape)==3:
gray=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
else:
gray=img
# Take both Sobel x and y gradients
sobelx=cv2.Sobel(gray,cv2.CV_64F,1,0)
sobely=cv2.Sobel(gray,cv2.CV_64F,0,1)
#calculate the magnitude of the gradients
magnitude=np.sqrt(sobelx**2+sobely**2)
#Rescale back to 8 bit integer
scaled_mag=np.uint8(magnitude*255/np.max(magnitude))
# Create a binary image of ones where threshold is met, zeros otherwise
binary_mag=binarize(scaled_mag,threshold)
return binary_mag
def dir_sobel(img,threshold=(0,np.pi/2)):
"""This function calculates the direction of Sobel gradient
Args:
img -- 3-D image or 2-D image
threshold -- A tuple of min and max value
returns:
binary -- Binary image of ones where threshold is met, zeros otherwise
"""
#If the image is Color image convert it to Grayscale
if len(img.shape)==3:
gray=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
else:
gray=img
## Take both Sobel x and y gradients
sobelx=cv2.Sobel(gray,cv2.CV_64F,1,0)
sobely=cv2.Sobel(gray,cv2.CV_64F,0,1)
#Calculate direction of absolute gradients using numpy.arctan2()
direction=np.arctan2(np.absolute(sobely),np.absolute(sobelx))
# Create a binary image of ones where threshold is met, zeros otherwise
binary_dir=binarize(direction,threshold)
return binary_dir
#Visualization of Gradient Thresholding
sobelx=abs_Sobel(img,'x',(20,100))
sobely=abs_Sobel(img,'y',(30,100))
sobel_mag=mag_sobel(img,(30,100))
sobel_dir=dir_sobel(img,(0.7,1.3))
f,axs=plt.subplots(2,2,figsize=(64,64))
ax=axs.ravel()
ax[0].imshow(sobelx)
ax[0].set_title('Sobel X',fontsize=50)
ax[1].imshow(sobely)
ax[1].set_title('Sobel Y',fontsize=50)
ax[2].imshow(sobel_mag)
ax[2].set_title('Sobel Magnitude',fontsize=50)
ax[3].imshow(sobel_dir)
ax[3].set_title('Sobel Direction',fontsize=50)
plt.tight_layout()
plt.savefig('output_images/gradient_threshold.png')
Combine Color and Gradient Thresholds¶
In [9]:
def pipeline(img):
#Imf is undistorted image
# Convert to HLS color space and separate the S channel
hls = hls_image(img)
s_channel = hls[:,:,2]
# Convert to HSV color space and separate the V channel
hsv = hsv_image(img)
v_channel = hsv[:,:,2]
# Threshold x gradient
sxbinary = abs_Sobel(img,'x',(30,100))
# Threshold color channel
s_thresh_min = 170
s_thresh_max = 255
s_binary = binarize(s_channel,(s_thresh_min,s_thresh_max))
v_thresh_min = 170
v_thresh_max = 255
v_binary = binarize(v_channel,(v_thresh_min,v_thresh_max))
# Combine the two binary thresholds
combined_binary = np.zeros_like(sxbinary)
combined_binary[((s_binary == 1) & (v_binary==1)) | (sxbinary == 1) ] = 1
return combined_binary
#Visualize Combined Binary Image
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image',fontsize=30)
ax2.imshow(pipeline(img),cmap='gray')
ax2.set_title('Binary Image',fontsize=30)
plt.savefig('output_images/binary_image.png')
Histogram Peaks¶
In [10]:
def hist(img):
# Lane lines are likely to be mostly vertical nearest to the car
bottom_half =img[img.shape[0]//2:,:]
# Sum across image pixels vertically
# i.e. the highest areas of vertical lines should be larger values
histogram = np.sum(bottom_half,axis=0)
return histogram
#images = glob.glob('test_images/*.jpg')
binary_warped=[]
#for idx, fname in enumerate(images):
#img = mpimg.imread(fname)
result=pipeline(img)
unwarped_image,Min=unwarp(result)
binary_warped.append(unwarped_image)
histogr=hist(unwarped_image)
f,(ax1,ax2)=plt.subplots(1,2,figsize=(16,9))
f.tight_layout()
ax1.imshow(unwarped_image,cmap='gray')
ax1.set_title('Binary Unwarped Image',fontsize=40)
ax2.plot(histogr)
ax2.set_title('Histogram Peaks',fontsize=40)
Out[10]:
Finding lanes-Sliding Window¶
In [11]:
def find_lane_pixels(binary_warped):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),
(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),
(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
#print(left_fit)
#print()
#print(right_fit)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
## Visualization ##
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
#plt.plot(left_fitx, ploty, color='yellow')
#plt.plot(right_fitx, ploty, color='yellow')
return out_img,left_fit,right_fit,ploty,left_fitx,right_fitx
#Visualization
for i in range(len(binary_warped)):
test_img=binary_warped[i]
out_img,left_fit,right_fit,ploty,left_fitx,right_fitx=fit_polynomial(test_img)
plt.imshow(out_img)
plt.title('Slding Window')
plt.show()
Search around Poly¶
In [12]:
def fit_poly(img_shape, leftx, lefty, rightx, righty):
### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty
def search_around_poly(binary_warped):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 100
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
### TO-DO: Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### Hint: consider the window areas for the similarly named variables ###
### in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
## Visualization ##
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Plot the polynomial lines onto the image
#plt.plot(left_fitx, ploty, color='yellow')
#plt.plot(right_fitx, ploty, color='yellow')
## End visualization steps ##
return result,left_fitx,right_fitx
for i in range(len(binary_warped)):
test_img=binary_warped[i]
out_img,left_fit,right_fit,ploty,left_fitx,right_fitx=fit_polynomial(test_img)
result,non,nom=search_around_poly(test_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.imshow(result)
plt.title('Lanes with Search window')
plt.show()
Radius of Curvature¶
In [13]:
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
def measure_curvature_pixels(left_fit,right_fit,ploty,left_fitx,right_fitx):
'''
Calculates the curvature of polynomial functions in pixels.
'''
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
# Calculation of R_curve (radius of curvature)
left_curverad = ((1 + (2*left_fit[0]*y_eval*ym_per_pix + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
right_curverad = ((1 + (2*right_fit[0]*y_eval*ym_per_pix + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
#Find the vehicle position
center = (left_fitx[-1] + right_fitx[-1])//2
veh_pos=img.shape[1]//2
distance_from_Center = (veh_pos - center)*xm_per_pix # Positive if on right, Negative if on left
return left_curverad, right_curverad,distance_from_Center
Image Processing Pipeline¶
In [14]:
def process(img):
#Undistort the image
undistorted_image=undistort(img)
#Apply color and Sobel thresholds
thresholded_image=pipeline(undistorted_image)
#Apply perspective transform
binary_warped,Minv=unwarp(thresholded_image)
#Apply sliding window and find the lanes and radius of curvature
out_img,left_fit,right_fit,ploty,left_fitx,right_fitx=fit_polynomial(binary_warped)
out_image,l,r=search_around_poly(test_img)
left_curverad, right_curverad,dx= measure_curvature_pixels(left_fit,right_fit,ploty,left_fitx,right_fitx)
#Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undistorted_image, 1,newwarp, 0.3, 0)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(result,'Left radius of curvature = %.2f m'%(left_curverad),(10,50), font, 1.5,(255,255,255),2,cv2.LINE_AA)
cv2.putText(result,'Right radius of curvature = %.2f m'%(right_curverad),(10,100), font, 1.5,(255,255,255),2,cv2.LINE_AA)
cv2.putText(result,'Vehicle position : %.2f m %s of center'%(abs(dx), 'left' if dx < 0 else 'right'),(10,150),
font, 1.5,(255,255,255),2,cv2.LINE_AA)
return result
In [15]:
images = glob.glob('test_images/*.jpg')
for idx, fname in enumerate(images):
img = mpimg.imread(fname)
result=process(img)
f,(ax1,ax2)=plt.subplots(1,2,figsize=(16,9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image',fontsize=35)
ax2.imshow(result)
ax2.set_title('Image with Lane area drawn',fontsize=35)
Create Video Pipeline¶
In [16]:
from moviepy.editor import VideoFileClip
from IPython.display import HTML
In [17]:
test_output = 'test_videos_output/project_video_output.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(process) #NOTE: this function expects color images!!
%time white_clip.write_videofile(test_output, audio=False)
In [18]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(test_output))
Out[18]: